Module 3: Business and Career Transformation Through AI
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In this post, I provide accurate answers and detailed explanations for Module 3: Business and Career Transformation Through AI of Course 1: Introduction to Artificial Intelligence (AI) – IBM Generative AI Engineering Professional Certificate
Whether you’re preparing for quizzes or brushing up on your knowledge, these insights will help you master the concepts effectively. Let’s dive into the correct answers and detailed explanations for each question.
Graded: AI Issues, Ethics and Bias
1. What is Al ethics?
- How to build and use Al in ways that align with human ethics and expectations
- An organization’s act of governing Al through its corporate instructions, staff, processes, and systems
- A multidisciplinary field that investigates how to maximize Al’s beneficial impacts while reducing risks and adverse impacts ✅
- How and why an Al system arrived at a particular outcome or recommendation
Explanation:
AI ethics is a broad and interdisciplinary field that addresses the moral and societal implications of AI. It focuses on minimizing potential harms and maximizing positive outcomes by studying how AI systems impact humans, society, and institutions.
2. In Al, what is fairness?
- An Al system’s ability to effectively handle exceptional conditions, like abnormal input or adversarial attacks
- An Al system’s ability to prioritize and safeguard humans’ privacy and data rights
- An Al system’s ability to show how and why it arrived at a particular outcome or recommendation
- An Al system’s ability to treat individuals or groups equitably ✅
Explanation:
Fairness in AI means ensuring that systems do not perpetuate biases or discriminatory practices. It involves designing models that provide equal opportunities and avoid favoring or disadvantaging any group or individual.
3. In Al, what is explainability?
- When appropriate information is shared with humans about how an Al system was designed and developed
- An Al system’s ability to show how and why it arrived at a particular outcome or recommendation ✅
- An Al system’s ability to treat individuals or groups equitably
- An Al system’s ability to effectively handle exceptional conditions, like abnormal input or adversarial attacks
Explanation:
Transparency in AI ensures that stakeholders understand how decisions are made. This involves clear documentation, explainability, and insight into the workings of the system, enabling accountability and trust.
4. In Al, what is privacy?
- An Al system’s ability to show how and why it arrived at a particular outcome or recommendation
- An Al system’s ability to treat individuals or groups equitably
- When appropriate information is shared with humans about how an Al system was designed and developed
- An Al system’s ability to prioritize and safeguard humans’ privacy and data rights ✅
Explanation:
Robustness in AI refers to the system’s capability to perform reliably under varied or unforeseen circumstances. This includes handling noisy, unexpected, or malicious data inputs without failure or significant degradation in performance.
5. In Al, what does bias do?
- Gives systematic disadvantages to certain groups or individuals ✅
- Identifies and addresses socio-technical issues raised by Al
- Solves problems faster
- Augments human intelligence
Explanation:
Bias in AI occurs when algorithms produce unfair outcomes due to prejudices in data, design, or implementation. These biases can lead to systematic disadvantages for specific groups, amplifying inequalities.
6. What is one potential cause of bias in an Al system?
- High-quality data sets
- Diverse teams
- Clear principles and pillars
- Implicit or explicit human bias ✅
Explanation:
AI systems are only as unbiased as the data and design decisions behind them. Human biases, whether intentional or unconscious, can influence datasets, model architecture, or decision-making criteria, introducing unfairness.
7. What is a regulation?
- A governance structure that works at scale
- A government rule enforceable by law ✅
- A multidisciplinary field that investigates how to maximize Al’s beneficial impacts while reducing risks and adverse impacts
- An organization’s act of governing through its corporate instructions, staff, processes, and systems
Explanation:
Regulations are legally binding rules established by governments to ensure compliance with standards, including those related to AI ethics, privacy, and safety.
8. What is Al governance?
- An organization’s act of governing through its corporate instructions, staff, processes, and systems ✅
- A government rule enforcible by law
- An Al system’s ability to prioritize and safeguard humans’ privacy and data rights
- A multidisciplinary field that investigates how to maximize Al’s beneficial impacts while reducing risks and adverse impacts
Explanation:
AI governance involves creating and enforcing internal policies to ensure AI systems align with ethical principles, organizational goals, and regulatory requirements.
9. When should developers, data scientists, and other people who work with Al consider ethics?
- Only when training the model
- Throughout the Al lifecycle ✅
- At the end of the Al lifecycle
- At the beginning of the Al lifecycle
Explanation:
AI ethics addresses the intertwined technical and societal impacts of AI. As AI reshapes industries, economies, and daily life, ethical considerations must account for both technological capabilities and societal consequences.
10. According to IBM's Betsy Greytok, what is the hottest topic in Al?
- How to use Al responsibly ✅
- How to use Al in hiring
- How to use Al in healthcare
- How to use Al in social media and marketing
Explanation:
Responsible AI use is a pressing issue as organizations strive to balance innovation with ethical considerations. This includes ensuring AI systems are fair, transparent, robust, and aligned with societal values.
11. What are the pillars of AI ethics?
- Explainability, fairness, robustness, transparency, privacy ✅
- Awareness, governance, operationalization
- Environmental impact, equitable impact, ethical impact
- Trust, efficiency, compliance
Explanation:
These pillars ensure AI systems operate ethically, emphasizing fairness in outcomes, explainability of decisions, robustness against errors, transparency in operations, and privacy protection.
12. In AI, what is privacy?
- An AI system’s ability to treat individuals or groups equitably
- An AI system’s ability to show how and why it arrived at a particular outcome or recommendation
- When appropriate information is shared with humans about how an AI system was designed and developed
- An AI system’s ability to prioritize and safeguard humans’ privacy and data rights ✅
Explanation:
Privacy in AI involves protecting user data and ensuring systems are designed to comply with privacy regulations, safeguarding sensitive information.
13. What is a first step toward mitigating bias in AI?
- Putting into place a governance structure that works at scale
- Developing different rules for different risks
- Assembling diverse teams ✅
- Designating a lead AI ethics official
Explanation:
Diverse teams bring varied perspectives, reducing the risk of bias during data collection, algorithm design, and system evaluation, promoting fairness.